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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Reinforced Similarity Integration in Image-Rich Information Networks
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Reinforced Similarity Integration in Image-Rich Information Networks

机译：图像丰富的信息网络中的增强相似性集成

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摘要

Social multimedia sharing and hosting websites, such as Flickr and Facebook, contain billions of user-submitted images. Popular Internet commerce websites such as Amazon.com are also furnished with tremendous amounts of product-related images. In addition, images in such social networks are also accompanied by annotations, comments, and other information, thus forming heterogeneous image-rich information networks. In this paper, we introduce the concept of (heterogeneous) image-rich information network and the problem of how to perform information retrieval and recommendation in such networks. We propose a fast algorithm heterogeneous minimum order k-SimRank (HMok-SimRank) to compute link-based similarity in weighted heterogeneous information networks. Then, we propose an algorithm Integrated Weighted Similarity Learning (IWSL) to account for both link-based and content-based similarities by considering the network structure and mutually reinforcing link similarity and feature weight learning. Both local and global feature learning methods are designed. Experimental results on Flickr and Amazon data sets show that our approach is significantly better than traditional methods in terms of both relevance and speed. A new product search and recommendation system for e-commerce has been implemented based on our algorithm.
机译：社交多媒体共享和托管网站，例如Flickr和Facebook，包含数十亿用户提交的图像。诸如Amazon.com之类的流行Internet商务网站也配有大量与产品相关的图像。另外，这种社交网络中的图像还伴随有注释，评论和其他信息，从而形成了异构图像丰富的信息网络。在本文中，我们介绍了（异构）富含图像的信息网络的概念以及如何在此类网络中进行信息检索和推荐的问题。我们提出了一种快速算法异构最小阶k-SimRank（HMok-SimRank），以计算加权异构信息网络中基于链路的相似性。然后，我们提出了一种综合加权相似度学习算法（IWSL），该算法通过考虑网络结构并相互增强链接相似度和特征权重学习来解决基于链接和基于内容的相似性。设计了局部和全局特征学习方法。在Flickr和Amazon数据集上的实验结果表明，就相关性和速度而言，我们的方法明显优于传统方法。基于我们的算法，实现了一种新的电子商务产品搜索和推荐系统。

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